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RAINFALL-RUNOFF MODELING USING ARTIFICIAL NEURAL NETWORKS : A CASE STUDY OF KHODIYAR CATCHMENT AREA

Author(s):

MAHESH B. SHRIVASTAV , SHANTILAL SHAH ENGG. COLLEGE, Bhavnagar; Prof. Haresh m. Gandhi, SHANTILAL SHAH ENGG. COLLEGE, Bhavnagar; Prof. Kashyap B. Gohil, SHANTILAL SHAH ENGG. COLLEGE, Bhavnagar; Prof. Nirav D. Acharya, SHANTILAL SHAH ENGG. COLLEGE, Bhavnagar; Mr. Jignesh A. Joshi, Salinity Control Sub-division, Bhavnagar

Keywords:

rainfall-runoff, modeling, ANN, algorithm, simulation, prediction.

Abstract

An Artificial Neural Network (ANN) methodology has been employed to develop Rainfall-Runoff Model as a function of rainfall, temperature, evaporation losses, infiltration losses and humidity for the Khodiyar catchment located in Amreli district, Gujarat, India,. The investigation of sensitivity of the modeling accuracy to the content and length of training data has been carried out. The comparison of ANN rainfall-runoff model was done favorably by obtaining results using existing techniques including statistical regression model. The ANN model provides a more systematic approach, reduces the length of calibration data, and shortens the time spent in calibration of the models, at the same time, it represents an improvement upon the prediction accuracy and flexibility of current methods.

Other Details

Paper ID: IJSRDV2I4329
Published in: Volume : 2, Issue : 4
Publication Date: 01/07/2014
Page(s): 520-525

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